A Review of Feature Extraction Software for Microarray Gene Expression Data

Table 8

Related work.

Software	Author	Motivation	Advantage
plsRglm (R package)	Bertrand et al. (2010) [32]	(i) To deal with incomplete datasets using cross-validation; (ii) To extend PLS regression to generalized linear models	(i) Provides formula support; (ii) Several new classes and their generics; (iii) Custom GLR models and graphics to assess the bootstrap based significance of the predictors
SVA-PLS	Chakraborty and Datta [30]	(i) To identify the genes that are differentially expressed between the samples from two different tissue types; (ii) To identify the hidden effects of the underlying latent factors in a gene expression profiling study	(i) Relatively better at discovering a higher proportion of the truly significant genes; (ii) Low error rate; (iii) High sensitivity and specificity
SlimPLS	Gutkin et al. [33]	To obtain a low dimensional approximation of a matrix that is “as close as possible” to a given vector	(i) Focuses solely on feature selection; (ii) Can be used as a pre-processing stage with different classifiers